Real-Time Information Dissemination
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Transcript of Real-Time Information Dissemination
2
Outline
• Introduction– What is information dissemination?
• Solutions– System model– Feedback Control Theory Solutions
• Results and Performance
• Summary
3
What is Information Dissemination?
• Publishers and Consumers of information, known as subscribers– Specify constraints on data, metadata– High subscriber count
• Sensor networks, surveillance systems, etc
• Controlled response time– Information is valuable in a specific time period
• Valuable Information at the Right Time (VIRT)
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Metadata Matching
• Metadata matching of constraints– Can’t reevaluation all subscriptions in each control
period– High-Priority tasks reevaluated within bounded
response time– Number of low priority tasks maximized, QoS
• Cost to evaluate a subscription varies at runtime– Changing number of publishers and consumers– Complexity of constraint– Unpredictable update arrival time
• How to achieve bounded response time?
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System Modeling
• System identification approach– r(k) = Σair(k-i) + Σbin(k-i)
– i from 1 to na and nb
• Use Least Squared Method with white noise to validate models– na = 0
– nb = 1
• System model r(k) = b1n(k-1)
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Root-Locus Design
• A PI controller– Integral is used to help eliminate steady-state
error– No derivative because it can amplify noise
• In the Z-domain F(z) = K1(z-K2)/(z-1)
– K1 = 1 / b1
– K2 = 0
• G(z) = z-1
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Model Variation
• System model is not perfect. Need to handle variation• The system model is approximately linear between response time and subscription reevaluation.• Model system as r(k) = gb1n(k-1)
– Execution time factor g = b`1/b1
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Stability
• Real system model, updated based on variation parameter
• G(z) = g / (z – (1 – g)– |1 – g| < 1
• Poles need to be within unit circle
• Stable as long as 0 < g < 2
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Steady State Error
• The steady state of the system is derived– limz->1 (z - 1) G(z) Rref (z / (z - 1))
– limz->1 gz / (z – (1-g)) Rref
– Rref
• Thus the system is guaranteed to achieve the response time if the system is stable
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Settling Time
• r(k) = (1 – g) r(k – 1) + gRref
• Settles when the systems converges to Rref ± 0.05
• The number of control periods required to settle is– k ≥ ln 0.05 / ln |1 - g|
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Implementation
• Assumption that updates arrive in 2 – 5 second intervals– Current work to relax this assumption
• 1 second set point
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Baselines
• OPEN– Fixed job budget– Can guarantee response time when estimated
execution time is correct– May violate timing when execution time is
underestimated
• Ad Hoc– Heuristic-based adaptive controller– Fixed step increments each control period based on
whether response time is above or below set point.
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Control Accuracy
• Starts using design time execution estimates (ie g=1)
• At time 1000, execution time increases to g=1.4
• At time 2000, g=1.8• OPEN fails to handle
changes in execution time
• PI controller meets deadlines and settling time design
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Comparison to Ad Hoc
• Starts out with g = 0.6• At 800s, g = 1• Ad Hoc takes 380s to settle vs 100s for PI controller
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Quality of Service (QoS)
• Open only considered when g ≤ 1
• PI controller offers better QoS than both OPEN and Ad Hoc
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Different Time Factors
• Relationship between response time and execution factor
• When g=2.6, the controller oscillates
• The response time stays close to the set point when the execution time factor is between 0 and 2
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Settling TimeResults vs Theoretical
• The experimental results are very close to the theoretical values predicted
• Experiments validate the theoretical analysis
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Summary
• Real-time information dissemination is used to share information in timely manner– Valuable Information at the Right Time (VIRT)
• PI controller maintains response time guarantees within settling time constraints with no steady state error
• Superior performance to OPEN and Ad Hoc (heuristic) controllers
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Chronos: Feedback Control of a Real Database System
Performance
Presented by
Ben Taylor
ECE 555Real-Time Embedded Systems
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Outline
• Introduction– What does a real-time database offer that
existing databases do not?
• Solutions– Feedback controller– Adaptive update policy
• Results and Performance
• Summary
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Why real-time databases?
• Existing databases have no notion of data freshness or timing deadlines– Stock trading system needs to keep prices up
to date while supporting reasonable response times
• Need soft real-time constraints on transactions while maintaining up-to-date data
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Controller Design
• If the system is overloaded the queue will tend toward unbounded growth
• If the system is underused, the queue size will tend to be small or empty
• The controlled variable is the service delay
• The manipulated variable is the ready queue size– If queue is full, transactions are not accepted
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Feedback Controller Overview
• At kth sampling, calculate delay error e(k) = Ds – d(k)
• Compute δq(k) based on e(k)• If δq(k) < 0
– Adjust adaptive update policy by increasing the period of cold data and increase δq(k) by (p[i]new – p[i])/p[i] until δq(k) ≥ 0 or period max
• q(k) = q(k-1) + δq(k)– 0 ≤ q(k) ≤ max_qsize
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Freshness Adaptation
• Control of data di, period of p[i]• Initially p[i] = 0.5 avi[i], absolute validity interval• AUR[i] = Access Frequency[i] / Update
Frequency[i]– di is hot if AUR[i] ≥ 1– Otherwise it is cold
• When increasing p[i]new = min(p[i]/AUR[i], Pmax)• After each update period, fvi[i]new = 2p[i]new,
where fvi[i] = avi[i] intially• avi[i] ≤ fvi[i]new ≤ 2Pmax
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System Identification
• Used to model relationship between the service delay and the queue size
• PI controller in the z domain
• Root Locus method in Matlab, similar to EUCON, to show controller is stable
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Performance
• Open - Pure Berkeley DB– Standard state-of-the-art database
• AC – Ad-hod Admission Control– Admission control in proportion to error
• FC-C – Feedback Control AC– Admission control with feedback loop
• FC-CU – Feedback Control AC + AUP– Adaptive temporal updates and admission
control with feedback loop
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Summary
• Real-time databases need to balance timely response with fresh data
• Designed feedback controller to manage backlog in system
• Adaptive update policy to manage freshness based on temporal data access and update patterns
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Comparisons
• Both use system identification for controller design, different models
• Chronos system maintains data freshness, a component not in the Information dissemination system
• Chronos system controller handles concurrency issues not present in Information dissemination system
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Critiques
• Both assume inter-arrival times of a limited window (2s, 5s) and (1s, 3s)
• Chronos systems states that workloads outside of operating range is reserved for a future work
• Information Dissemination assumes a given number of subscriptions will have the same cost as a different set of subscriptions the same size